W. Scott Langford, PhD

W. Scott Langford, PhD

Assistant Professor

TX_County_Political_Panel_2000_2024 — Codebook

TX_County_Political_Panel_2000_2024 — Codebook

Unit of observation: Texas county × presidential-election year. Coverage: all 254 counties × 7 elections (2000, 2004, 2008, 2012, 2016, 2020, 2024) = 1,778 rows. Role in course: the second spine dataset — a political-science companion (for PS 3315) to the city sales-tax panel. Same methods (descriptives, t-tests, regression) on political/socioeconomic outcomes. Join key: 5-digit county FIPS (fips, zero-padded string, Texas = 48xxx).

Variables

| Variable | Description | Units | Source | |—|—|—|—| | fips | County FIPS (5-digit) | — | — | | county | County name | — | Census | | year | Presidential election year | 2000–2024 | — | | metro_status | Metro vs. Non-Metro | category | OMB/Census CBSA 2023 | | cbsa_type | Metropolitan / Micropolitan / Neither | category | OMB/Census CBSA 2023 | | population | Total population (that year) | persons | Census PEP | | voting_age_pop | Voting-age population (18+) | persons | Census PEP (2012–2024 only) | | total_votes | Total presidential votes cast | votes | MIT Election Lab / county returns | | rep_votes | Republican presidential votes | votes | MIT Election Lab / county returns | | dem_votes | Democratic presidential votes | votes | MIT Election Lab / county returns | | rep_two_party_share | rep / (rep + dem) | proportion | derived | | margin_rep | rep_two_party_share − dem_two_party_share | proportion | derived | | turnout_vap | total_votes / voting_age_pop | proportion | derived (2012–2024 only) | | pct_hispanic | % Hispanic (any race) | percent | Census PEP | | pct_black | % Black alone | percent | Census PEP | | pct_white_nh | % White, non-Hispanic | percent | Census PEP | | median_age | Median age | years | Census PEP (2012–2024 only) | | pop_density | population / land area (sq mi) | per sq mi | derived (Census Gazetteer 2023 land area) | | unemployment_rate | Unemployment rate (that year) | percent | USDA ERS (BLS LAUS) | | pct_bachelors_plus | % adults 25+ with a bachelor’s+ | percent | USDA ERS (nearest ACS period) | | median_household_income | Median household income (that year) | $ | Census SAIPE | | poverty_rate | Poverty rate, all ages (that year) | percent | Census SAIPE | | vote_center_adopt_year | First year the county participated in the Texas Countywide Polling Place Program (vote centers); blank = not adopted as of the data | year | TX Secretary of State (see below) | | vote_center | Time-varying treatment indicator: 1 if the election year ≥ vote_center_adopt_year, else 0 | 0/1 | derived |

Sources (all free; no API key)

  • Political: MIT Election Lab county presidential returns — 2000–2016 via the official MEDSL county-returns GitHub mirror; 2020 & 2024 via the tonmcg county-results GitHub repo. (Canonical dataset: Harvard Dataverse doi:10.7910/DVN/VOQCHQ, which is guestbook-gated; the GitHub mirrors carry the same returns.)
  • Demographic: U.S. Census Population Estimates Program (county, by year) — population, voting-age (18+), race/ethnicity, median age — across the 2000–2010, 2010–2020, and 2020–2024 vintages. Land area from the Census County Gazetteer (2023). Metro status from the OMB/Census CBSA delineation (2023).
  • Socioeconomic: Census SAIPE (median household income + poverty rate, by year); USDA ERS county data sets (unemployment by year, from BLS LAUS; educational attainment by nearest ACS period).
  • Vote centers (vote_center*): Texas Secretary of State, Countywide Polling Place Program (CWPP). First-participation election dates are chained from the SoS biennial reports under Tex. Elec. Code §43.007(j) (83rd–88th Legislatures, covering 2006–2022) plus the current CWPP approved-county page for 2023–2026 entrants. Program history: pilot 2006 (HB 758); permanent 2009 (HB 719). The 100-county lookup is data/vote_center_adoption.csv; rebuild with scripts/add_vote_centers.py. Academic background: Stein & Vonnahme; Cortina & Rottinghaus (2019), Research & Politics, doi:10.1177/2053168019864224.

Year-code decodings used (Census PEP)

  • 2020–2024 files (cc-est2024-*): YEAR 2=2020, 6=2024.
  • 2010–2020 files (CC-EST2020-*): YEAR 4=2012, 8=2016 (1=Apr-2010 census, 2=Jul-2010, 3=2011, …).
  • 2000–2010 intercensal files: used for 2000/2004/2008 population, race, and ethnicity.

Limitations (teaching points — documented, never fabricated)

  1. Turnout, voting-age population, and median age exist only for 2012–2024. The 2000–2010 PEP age file uses 5-year bins that straddle age 18, so a clean 18+ count (the turnout denominator) can’t be derived for 2000/2004/2008. Those cells are blank.
  2. turnout_vap exceeds 1.0 in 4 micro-county rows (Loving 48301, McMullen 48311, in 2020 & 2024). These are faithful, not bugs — a known artifact of PEP population estimates for counties of a few dozen people. Retained as-is; a good outlier/measurement-error lesson.
  3. pct_bachelors_plus uses the nearest ERS/ACS education period (only a few periods exist), so some election years share a value.
  4. pop_density uses 2023 land area for all years (land area is ~stable; only the current Gazetteer vintage was used).
  5. Demographic shares are stitched from mixed PEP vintages at the decade boundaries; small discontinuities can occur around 2010/2020.
  6. vote_center_adopt_year is a teaching dataset. The county→year pairs were transcribed from the SoS §43.007(j) report pages (not the raw PDF tables); for research-grade use, re-verify each date against the original report PDFs. “Adoption year” = first election of participation (the SoS also tracks a separate, slightly later “successful designation” date). 100 of 254 counties had adopted as of the data (cumulative: 45 by 2016, 77 by 2020, 99 by 2024).

How it powers the course (PS 3315 angle)

  • Descriptives/distributions: turnout, partisan share, income, poverty across the 254 counties.
  • Independent t-test: turnout or Republican share, Metro vs. Non-Metro counties.
  • Paired t-test: a county’s turnout or partisan share, 2016 vs. 2020 (or 2020 vs. 2024).
  • Regression: turnout ~ median income / % bachelor’s; partisan share ~ demographics (with an omitted-variable-bias discussion).
  • Trends: partisan realignment and turnout change 2000 → 2024.
  • Causal designs (PA 5312 Case B): vote_center is a staggered, time-varying treatment — supports difference-in-differences (adopters vs. not-yet/never-adopters) and interrupted time series (a county’s turnout before/after its own adoption year). Caution: 2020’s pandemic turnout surge and adopter self-selection confound a naive 2×2 (it gives roughly −1.7 pts for 2017–2020 adopters), which is itself a teachable point.

Built 2026-06-02. Reproduce with build_county_panel.py (political + demographic), then add_saipe.py (income + poverty), then add_vote_centers.py (vote-center treatment variables). Raw inputs in _raw_county/.